This invention relates generally to touch sensitive systems, and more particularly to identifying a location of a touch on a touch sensitive system that uses a bending-wave touch panel.
Touch panels are used to provide two-dimensional coordinate information. One example may be an opaque track pad while another example may be a transparent touchscreen placed in front of a display such as a liquid crystal display. Touch panels may be based on a variety of touch technologies including four-wire and five-wire resistive, capacitive, infrared and surface acoustic wave types, as well as bending-wave touch technologies.
Bending waves may also be referred to as “flexural waves” as well as “lowest order anti-symmetric Lamb waves” which is sometimes abbreviated as “A0.” Bending waves in a plate, such as a touchscreen substrate, are characterized by motion that is largely perpendicular to the surface and essentially the same at all depths with respect to the surface. For example, touch detection has been proposed via finger absorption of ultrasonic frequency Lamb waves in the MHz frequency range. However, most recent work with bending wave touch systems uses frequencies well below the MHz range including the audio range.
In one approach in International Publication Number WO 00/38104 (Intelligent Vibrations, published 2000), bending wave touch systems may detect a touch based on a tap of an object, such as a key or finger, used to excite bending waves in a substrate. These bending waves induce electrical signals in piezoelectric elements or sensors (piezos) bonded to the substrate. These signals are captured by electronics and processed to determine (X,Y) coordinates of the touch position, such as by using time-of-flight methods to extract touch coordinate information from piezo signals.
An approach in International Publication Number WO 01/43063 (Soundtouch Limited, published 2001) may be described as “acoustic fingerprinting”. Templates are acquired that correspond to frequency transforms of piezo signals resulting from calibration touches on a substrate at various distances “D” from a piezo that is bonded to the substrate. In one example, each template has a horizontal axis representing frequency and a vertical axis representing normalized amplitude magnitude. During real time touch operation of the system, a similar normalized-amplitude-versus-frequency profile (as discussed further below) is generated for a touch event and compared with a library of templates. The distance from the touch to the piezo is determined by the value of “D” corresponding to the best matching template.
In another approach discussed in British Publication Number GB 2385125 (Hardie-Bick, published 2003), a normalized amplitude versus frequency profile is computed from a live touch and compared to a library of template profiles. The template with the best matching score is found. In this case, however, the normalized amplitude profiles are not just a function of the distance “D” between the touch and a piezo, but rather a function of (X,Y) touch coordinates in two dimensions. The normalized amplitudes, however, are sensitive to the details of the power spectrum of the touch force which varies with the style and implement used to make the touch. Therefore, the normalized amplitudes are sensitive to factors other than the (X, Y) coordinate location of the touch.
In another approach, International Publication Number WO03/067511 (Soundtouch Limited, published 2003), identifies touch locations using phase-difference profiles. The following mathematical notation is provided to more precisely define the terms normalized amplitude profiles and phase-difference profiles.
In one example, an operator touches a touch panel having four piezos from which two analog signals are generated. (In other examples, more or less than four piezos, sensors or microphones may be used.) Each of the signals, namely S1(t) and S2(t), are functions of time “t”. The signals S1(t) and S2(t) are digitized and frequency transformed, resulting in corresponding signals S1(ω) and S2(ω) in the frequency domain where ω=2πf is angular frequency. Any function or algorithm based on angular frequency ω is easily converted to a function or algorithm based on frequency f and vice versa; below the term “frequency” is interpreted broadly to include both frequency and angular frequency. For example, S1(ω) and S2(ω) may represent the frequency transforms of S1(t) and S2(t). By way of example only, S1(ω) and S2(ω) may be computed as a discrete frequency transform over a finite time interval such as provided by a Fourier transform or FFT (Fast Fourier Transform). S1(t) and S2(t) are real functions, while S1(ω) and S2(ω) are complex functions with both real parts Re{S1(ω)} and Re{S2(ω)} as well as imaginary parts Im{S1(ω)} and Im{S2(ω)}. This gives a total of four real functions of frequency. Such a complex number representation is required because at each frequency, the signal amplitude has both a magnitude and a phase. The complex functions S1(ω) and S2(ω) may be represented as products of a positive real magnitude and a complex phase factor of unit magnitude as shown below in Equation (Equ.) 1 and Equ. 2.S1(ω)=A1(ω)·eiφ1(ω)=A1(ω)·cos(φ1(ω))+i·A1(ω)·sin(φ1(ω))  Equ. 1S2(ω)=A2(ω)·eiφ2(ω)=A2(ω)·cos(φ2(ω))+i·A2(ω)·sin(φ2(ω))  Equ. 2Here A1(ω)=|S1(ω)| and A2(ω)=|S2(ω)| are the positive real magnitudes of the signal amplitudes, φ1(ω) and φ2(ω) are the phases of the complex amplitudes S1(ω) and S2(ω), and “i” represents the square root of negative one. For Equ. 3 and 4 below, A1MAX is the maximum value of the amplitude magnitude A1(ω) over the frequency range and A2MAX can be similarly defined. Equ. 3 and Equ. 4 define the normalized amplitude profiles a1(ω) and a2(ω) for the signals S1(t) and S2(t), wherein a1(ω) and a2(ω) are normalized to have maximum values of one.a1(ω)=A1(ω)/A1MAX  Equ. 3a2(ω)=A2(ω)/A2MAX  Equ. 4Equ. 5 defines the phase difference profile Δφ(ω).Δφ(ω)=φ2(ω)−φ1(ω)  Equ. 5
For clarity of presentation the above functions of frequency such as the amplitude magnitudes A1(ω) and A2(ω) and the phase difference Δφ(ω) are represented as continuous functions of frequency ω. In a practical engineering implementation, the mathematical continuum of frequencies ω is replaced by a finite set of discrete frequencies, for example, ωk where the integer index k ranges from one to a perhaps large but finite integer K. Likewise, continuous functions of frequency are replaced by sets of values, such as A1(ωk) corresponding to the discrete frequencies ωk.
It has been observed that the phase difference profile Δφ(ω) performs much better as an acoustic fingerprint than the normalized amplitudes a1(ω) and a2(ω). For example, ΔΦ(ω) represents a template phase difference profile corresponding to a calibration touch at a known location (X0,Y0). When the live acoustic fingerprint of the phase difference profile Δφ(ω) is a close match to a template phase difference profile ΔΦ(ω) (Φ indicating calibration signal and φ indicating live signal), it is indicated that the coordinates (X,Y) of the live touch are close to the coordinates (X0,Y0) of the calibration touch that generated the template phase difference profile ΔΦ(ω).
As currently demonstrated by existing products, bending-wave touch systems based on acoustic fingerprinting provide good touch performance in combination with a number of other desirable product features such as high optical transparency, wear resistance, immunity to water contaminants and compact mechanical design. This is particularly true for larger sized systems, such as desktop sized touch/display systems based on, for example, 15-inch (approximately 380 mm) diagonal liquid crystal displays. However for some applications, there remains a need for improved acoustic fingerprint definitions and matching algorithms. In particular, for smaller sensor sizes, such as those used in handheld applications and point-of-sale applications, the phase difference profile Δφ(ω) by itself is not always sufficient to reliably extract touch coordinates when the signals S1(t) and S2(t) are subject to ambient noise. Furthermore, in some applications, brief moments of corrupted signal data can be a major problem due to generation of false touch coordinates.
There remains a need for improved acoustic fingerprints and matching algorithms, as well as improved identification of corrupted signal data, for use in bending-wave touch systems.